A framework for dealing with uncertainty due to model structure error

Jens Christian Refsgaard, Jeroen P. van der Sluijs, James Brown, Peter van der Keur

Research output: Contribution to journalArticleResearchpeer-review

354 Citations (Scopus)

Abstract

Although uncertainty about structures of environmental models (conceptual uncertainty) is often acknowledged to be the main source of uncertainty in model predictions, it is rarely considered in environmental modelling. Rather, formal uncertainty analyses have traditionally focused on model parameters and input data as the principal source of uncertainty in model predictions. The traditional approach to model uncertainty analysis, which considers only a single conceptual model, may fail to adequately sample the relevant space of plausible conceptual models. As such, it is prone to modelling bias and underestimation of predictive uncertainty. In this paper we review a range of strategies for assessing structural uncertainties in models. The existing strategies fall into two categories depending on whether field data are available for the predicted variable of interest. To date, most research has focussed on situations where inferences on the accuracy of a model structure can be made directly on the basis of field data. This corresponds to a situation of 'interpolation'. However, in many cases environmental models are used for 'extrapolation'; that is, beyond the situation and the field data available for calibration. In the present paper, a framework is presented for assessing the predictive uncertainties of environmental models used for extrapolation. It involves the use of multiple conceptual models, assessment of their pedigree and reflection on the extent to which the sampled models adequately represent the space of plausible models.

Original languageEnglish
Pages (from-to)1586-1597
Number of pages12
JournalAdvances in Water Resources
Volume29
Issue number11
DOIs
Publication statusPublished - Nov 2006

Keywords

  • Conceptual uncertainty
  • Environmental modelling
  • Model error
  • Model structure
  • Pedigree
  • Scenario analysis

Programme Area

  • Programme Area 2: Water Resources

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